Exponential Growth
The exponential growth of AI, like many forms of technology, can be largely attributed to a phenomenon known as Moore's Law, which predicts that the number of transistors (a key component for processing information) on a chip doubles approximately every two years. This leads to a rapid improvement in computational power, enabling more complex and powerful AI algorithms to be developed and implemented.
Key factors contributing to the exponential growth of AI include:
Increase in computational power: The growth and development of AI have been heavily dependent on the increasing processing capabilities of computers. With the advent of more powerful processors and hardware accelerators like GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units), AI models can process more data and execute more complex operations than ever before.
Availability of data: The proliferation of the internet and digital devices has led to an explosion in the amount of data produced and collected. This big data is essential for training AI systems, especially those based on machine learning. These models learn and improve from the data they process, leading to an exponential growth in their capabilities.
Advances in machine learning algorithms: Machine learning, a subset of AI, has seen significant advancements in the last few decades. Techniques such as deep learning, which can model high-level abstractions in data by using multiple processing layers with complex structures, have dramatically improved the capabilities of AI systems.
Investment and research: As the potential applications and benefits of AI become clearer, there's been a significant increase in investment in AI from both private and public sectors. This funding fuels research and development, leading to breakthroughs and advancements at an accelerated pace.
Democratization of AI: Tools and frameworks for developing AI (like TensorFlow, PyTorch, etc.) have become more accessible and user-friendly. This has allowed a wider range of people to contribute to AI development, further accelerating its growth.
Interdisciplinary integration: AI has been integrated with other disciplines such as biology, physics, and psychology to create hybrid models that can better simulate and predict complex real-world phenomena.
The combination of these factors has created a positive feedback loop: advancements in one area often fuel progress in others, leading to a rapid, exponential growth of AI. However, it's important to note that there are challenges and potential drawbacks associated with this rapid growth, such as ethical considerations, the potential for job displacement, and issues of security and privacy.
Moore’s Law
Moore's law is the observation that the number of transistors in a dense integrated circuit doubles about every two years.
Note that the graph is shown on a logarithmic y axis scale so that a straight line up is actually exponential growth,.
AI Computing Power Growth is Exceeding Moores Law
Since 2012, the growth of AI computing power has risen to doubling every 3.4 months, exceeded Moore’s law.
Source: https://hai.stanford.edu/sites/default/files/ai_index_2019_report.pdf
AI Capabilities Relative to Human Performance
Per the chart below, AI models are rapidly approaching and exceeding human performance in a number of dimensions.
Stages of AI Growth
As the power of Machine Learning and AI grows, so will its capabilities and impacts as AI proceeds through major stages: